SUMMARY The availability of predictive in vitro models of human tumors designed to accurately recapitulate key aspects of human pathophysiology would be transformative to cancer research and pre-clinical validation of new therapeutic modalities. We assembled an interdisciplinary team of leading experts in bioengineering, cancer biology, systems biology, pathology and oncology to establish such model. Based on extensive prior work, we propose to develop a state-of-the art ?cancer patient on a chip? of invasive human breast carcinoma. The tumor will be physiologically integrated with their cognate metastatic sites (lung, liver, bone) via vascular perfusion containing circulating cells. The tumor compartment will be established directly from surgical specimens grown in 3D, organotypic conditions while target metastatic sites and vasculature will be established from blood-derived, patient-matched iPS cells, under an active institutional review board protocol. The system is imaging compatible and supports long-term culture (up to 12 weeks). Biological fidelity and heterogeneity of primary and metastatic sites, as implemented in the context of such vascularized multi-tissue platform, will be validated by single-cell analyses vs. the corresponding native tumor. For these studies, we will recruit a cohort of patients with metastatic tumors. Our ultimate goal is to demonstrate utility of the platform in elucidating mechanisms of tumor progression and drug resistance, by testing drug panels predicted by a novel RNA-seq-based, NY CLIA certified methodology (OncoTreat). Our hypothesis is that our system will recapitulate key properties of the metastatic breast adenocarcinoma and enable identification of target proteins that mechanistically drive tumor progression and drug sensitivity/resistance. Three specific aims will be pursued in a highly integrated fashion: Aim 1: Bioengineer a 3D human breast carcinoma model and metastasis host tissues; Establish a model of metastasis in an integrated patient-on-a-chip platform; Aim 3: Elucidate master regulators and predict drug sensitivity in metastatic cells using the ?cancer patient on a chip? model. We anticipate that this platform would have broad utility in cancer research and in patient-specific testing of new therapeutic modalities.